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Practical Assignment Optimization: Waste Collection van x naar u Kevin van Blokland, MSc 22 november 2016
Practical Assignment Optimization:
Waste Collection
van x naar u
Kevin van Blokland, MSc
22 november 2016
Practical assignment: Van Gansewinkel Groep
“Waste doesn’t exist”
Offers integral waste services and reliable environmental solutions for 9
European countries
Specialized in collecting, transferring, recycling and processing waste
Copyright CQM B.V.
Practical assignment in a nutshell
Given the following inputs:
A number of waste collection vehicles
A waste disposal location
A list of orders of waste collection points
A distance matrix between waste collection points
Create the following plan:
Create a weekly schedule for each vehicle such that as much orders as possible
are fulfilled and the total use time of the waste collection vehicles is minimized
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Orders Van Gansewinkel Groep
Van Gansewinkel Groep has various subscription types for sale for the
collection of waste:
Fixed schedule for customers
- Frequency varies from five times per week to once every 12 weeks.
- For this assignment the frequency varies from one to five times per week.
One ore more containers with a volume of 140 to 5000 liters
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Valid frequencies for waste collection
When a customer subscribes for example for garbage collection twice per week,
the two collection times should be more or less evenly spread over the week
Table below shows valid waste collection patterns:
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Indication Frequency Valid patterns
1PWK Once per week mo, tu, we, th, fr
2PWK Twice per week mo_th, tu_fr
3PWK Three times per week mo_we_fr
4PWK Four times per week mo_tu_we_th, mo_tu_we_fr,
mo_tu_th_fr, mo_we_th_fr,
tu_we_th_fr
5PWK Five times per week mo_tu_we_th_fr
Fleet Van Gansewinkel Groep
2 vehicles
Both vehicles available on Monday to Friday from 6:00h until 18:00h
Outside the times above the vehicles have to be empty and be present at the
waste disposal location
The capacity per vehicle is 20.000 liters
The volume of waste is reduced by a factor five when it is collected.
For example a container of 1000 liters corresponds with a volume of 200 liter in
the garbage collection vehicle.
- Containers usually are not completely filled
- Waste is thickened by compressing it
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Disposal of waste
Collected waste has to be taken to the waste processor which is located
at the waste disposal location
Disposal of waste always takes 30 minutes
It is allowed to dispose waste more than once per day
The two vehicles may dispose waste at the same time
When waste is disposed at the end of the day, the waste disposal has to
be finished before 18:00h
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Objective of the assignment
Make routes whereby for each customer the waste is collected with the agreed
frequency
The total use time of the vehicles has to be minimized.
The total use time is the sum of:
- Time of emptying containers
- Total travel time
- Time of disposing waste
Orders have to be planned completely or not be planned:
- Not planning an order yields a penalty of 3 times the total emptying time. The total
emptying time is defined as the single emptying time multiplied by the number of
times an order has to be collected.
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Datasets
Orderbestand.txt
AfstandenMatrix.txt
Copyright CQM B.V.
Order Unique number for the order
Plaats Location
Frequentie Collection frequency of the order
AantContainers Number of containers that have to be collected each time. All containers
have the same type (volume)
VolumePerContainer Volume per container at the customer (uncompressed)
LedigingsDuurMinuten Emptying time of this order (all containers together)
MatrixID Reference to the distance matrix
Xcoordinaat X coordinate of this order
Ycoordinaat Y coordinate of this order
MatrixID1 Reference of the from location
MatrixID2 Reference of the to location
Afstand Distance from the “from location” to the “to location” in meters
Rijtijd Travel time from the “from location” to the “to location” in seconds
Solution format
A route is a per vehicle (1 and 2) per day (1 through 5) ordered list of orders that
are collected after each other. The route also contains one or more waste
disposal moments.
Format file:
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Vehicle; Day; Sequence number; Order
Vehicle {1, 2}
Day {1, …, 5} (1=mo, 2=tu, 3=we, 4=th, 5=fr)
Sequence number {1, 2, … } Sequence number from which the sequence of the route per vehicle
per day can be deduced. The sequence may start per vehicle each day at 1.
Order Order from Orderbestand.txt. When the vehicle goes to dispose waste, this
should be indicated with order number 0.
Waste disposal location
Van Gansewinkel Groep Oost Brabant
Den Engelsman 4
Maarheeze
MatrixID = 287
Copyright CQM B.V.
Omitted conditions
In the project CQM executed for Van Gansewinkel Groep the following conditions
also had to be taken into account:
Continuity for the customer (collect garbage at the approximately the same time
each day)
Continuity for the driver (each day more or less the same addresses, whereby
some addresses are deleted or added)
Additional frequencies for orders
Different waste disposal locations with various disposal time and costs
Fuel costs
Vehicles are not homogeneous
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Results project
Saving in costs
Saving in kilometers
Saving in planning time
- At first a full time planner was required for about 3 to 4 months,
this has been reduced to approximately one month
More insight in the effects of new customers
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Informatie over CQM
van x naar u
Profiel CQM
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CQM
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CQM’s values Commitment
Passion
Personal development
Working together
Creativity Results
Referenties
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Warmtebedrijf Rotterdam
Warmtebedrijf Rotterdam brengt warmte die
over is bij industriële processen in de haven,
via een efficiënt leidingennetwerk tot in de stad.
Warmte van vuilverbrander AVR
Warmte wordt gebruikt voor de verwarming van
woningen in de Rotterdamse wijken Hoogvliet,
Charlois, en door het Maasstad Ziekenhuis
In dienst sinds 1 oktober 2013
Bron: Warmtebedrijf Rotterdam
Netwerk van transportleidingen
Bron: Warmtebedrijf Rotterdam
Vraagstelling
Ondersteuning nodig bij
- Optimalisatie
- Reconciliatie (afrekening met EON)
- Analyse
voor de dispatching van DNWW
Dispatching: productieplan
- Op welke momenten warmte maken?
- Warmtebufferstrategie
Ondersteuning in de vorm van een tool in AIMMS
Bron: Warmtebedrijf Rotterdam
BiedOptimaal
How growers become energy suppliers. How does that work?
The growers perspective
The choices the grower can take
- Produce heat upfront and store it in a buffer
- Produce heat just in time
- Use boiler to produce heat
- Use CHP to produce heat and electricity
Heat and C02 for own usage, but electricity can be sold
What is needed for the 10 o’clock decision?
- Make a production plan for tomorrow and
sell at the electricity today before 10am.
- Based on
- Current situation
- Plan for today
- Forecasts for future heat, power consumption
- Forecasts for future gas and electricity prices
Optimization to make the ultimate bid at the energy market
Frog AGV Systems
Goal
Handle the AGV flows through the elevator system
as fast as possible taking into account due times and
priorities.
Complexities
Due times should be met
Some flows have a higher priority than others
Different floor and elevator layouts
Maximum calculation time (0.5 seconds)
Tekst 100%
TEKST NIVEAUS
Leestekst (16 pt.)
1
2
4
5
Bullet (16 pt.)
• Sub-bullet 1 (14 pt.)
Kopje paars (16 pt.)
Level up
Level down
6 Kopje blauw (16 pt.)
3 o Sub-bullet 2 (14 pt.)
What are we modelling/simulating
Model objects
Elevator tower
Queues
AGV Traffic
Scope
We restrict ourselves to
the direct surroundings of
a single elevator bank.
AGV becomes known to
us when arriving at an
elevator LSP.
Then we tell the AGV (and
the elevators) what to do.
Tekst 100%
TEKST NIVEAUS
Leestekst (16 pt.)
1
2
4
5
Bullet (16 pt.)
• Sub-bullet 1 (14 pt.)
Kopje paars (16 pt.)
Level up
Level down
6 Kopje blauw (16 pt.)
3 o Sub-bullet 2 (14 pt.)
Inzet van deep learning voor herkennen van defecten
Copyright © CQM
42
Defects Onbeschadigd spoor
Squat B
500x500 pixels
Tekst 100%
TEKST NIVEAUS
Leestekst (16 pt.)
1
2
4
5
Bullet (16 pt.)
• Sub-bullet 1 (14 pt.)
Kopje paars (16 pt.)
Level up
Level down
6 Kopje blauw (16 pt.)
3 o Sub-bullet 2 (14 pt.)
Ontwerp van een neuraal netwerk
43
P
Tekst 100%
TEKST NIVEAUS
Leestekst (16 pt.)
1
2
4
5
Bullet (16 pt.)
• Sub-bullet 1 (14 pt.)
Kopje paars (16 pt.)
Level up
Level down
6 Kopje blauw (16 pt.)
3 o Sub-bullet 2 (14 pt.)
Deep learning machine
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Beste videokaart van het moment
Machine uitbreidbaar naar 3-way SLI
3600 cores
Trainen van grote dataset duurt
enkele minuten in plaats van dagen!
Tekst 100%
TEKST NIVEAUS
Leestekst (16 pt.)
1
2
4
5
Bullet (16 pt.)
• Sub-bullet 1 (14 pt.)
Kopje paars (16 pt.)
Level up
Level down
6 Kopje blauw (16 pt.)
3 o Sub-bullet 2 (14 pt.)
Resultaten
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Nacht van Eindhoven
Competitie tussen Universiteiten, georganiseerd door CQM
https://www.facebook.com/nachtvaneindhoven
Team van Universiteit Utrecht won editie 2015!
Roel van den Broek, Geertièn de Vries & Peter Ypma
